No-Regret Slice Reservation Algorithms

Emerging network slicing markets promise to boost

the utilization of expensive network resources and to unleash

the potential of over-the-top services. Their success, however,

is conditioned on the service providers (SPs) being able to bid

effectively for the virtualized resources. In this paper we consider

a hybrid advance-reservation and spot slice market and study

how the SPs should reserve slices in order to maximize their

performance while not exceeding their budget. We consider this

problem in its general form, where the SP demand and slice

prices are time-varying and revealed only after the reservations

are decided. We develop a learning-based framework, using the

theory of online convex optimization, that allows the SP to employ

a no-regret reservation policy, i.e., achieve the same performance

with a hypothetical policy that has knowledge of future demand

and prices. We extend our framework for the scenario the SP

decides dynamically its slice orchestration, where it additionally

needs to learn which resource composition is performance -

maximizing; and we propose a mixed-time scale scheme that

allows the SP to leverage any spot-market information revealed

between its reservations. We evaluate our learning framework

and its extensions using a variety of simulation scenarios and

following a detailed parameter sensitivity analysis.